Evidence factors from multiple, possibly invalid, instrumental variables
利用多个可能无效的工具变量构造证据因子AI 解读 ↓
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利用多个可能无效的工具变量构造证据因子
- 研究问题
- 存在多个候选工具变量且部分可能无效时，如何构造依赖不同假设、近乎独立的因果分析，并减少分析顺序的影响？
- 方法与思路
- 作者提出平衡区组设计，通过工具变量之间的平衡处理可能的排除限制违反，并为嵌套候选工具变量提出分层方法。
- 材料陈述的贡献
- 摘要称，平衡区组设计可在多种分析策略下构造不受顺序约束的近似证据因子，并展示教育与疟疾效应应用。
- 阅读关注点
- 应核对平衡如何影响排除限制偏离、证据因子的近似独立条件，以及嵌套工具变量分层方法仍依赖哪些有效性假设。
- 阅读边界
- 仅据版本未注明的 OpenAlex 摘要；不能据此认定任意无效工具变量均可使用，应用效应数值与识别细节未说明。
原文摘要
Valid instrumental variables enable treatment effect inference even when selection into treatment is biased by unobserved confounders. When multiple candidate instruments are available, but some of them are possibly invalid, the previously proposed reinforced design enables one or more nearly independent valid analyses that depend on very different assumptions. That is, we can perform evidence factor analysis. However, the validity of the reinforced design depends crucially on the order in which multiple instrumental variable analyses are conducted. Motivated by the orthogonality of balanced factorial designs, we propose a balanced block design to offset the possible violation of the exclusion restriction by balancing the instruments against each other in the design, and demonstrate its utility for constructing approximate evidence factors under multiple analysis strategies free of the order imposition. We also propose a novel stratification method using multiple, nested candidate instruments, in which case the balanced block design is not applicable. We apply our proposed methods to evaluate (a) the effect of education on future earnings using instrumental variables arising from the disruption of education during World War II via the balanced block design, and (b) the causal effect of malaria on stunting among children in Western Kenya using three nested instruments.